Comparison and validation of different models and variable selection methods for predicting survival after canine parvovirus infection.
CPV
diagnostic
lethality
machine learning
outcome prediction
Journal
The Veterinary record
ISSN: 2042-7670
Titre abrégé: Vet Rec
Pays: England
ID NLM: 0031164
Informations de publication
Date de publication:
31 Oct 2020
31 Oct 2020
Historique:
received:
22
11
2018
revised:
04
02
2020
accepted:
01
03
2020
pubmed:
15
3
2020
medline:
18
5
2021
entrez:
15
3
2020
Statut:
ppublish
Résumé
Canine parvovirus (CPV) represents one of the major infections in dogs. While supportive therapy significantly reduces mortality, other approaches have been reported to provide significant benefits. Unfortunately, the high cost of these treatments is typically a limiting factor. Consequently, a reliable prognostic tool allowing for an informed therapeutic approach would be of great interest. However, current methods are essentially based on 'a priori' selection of predictive variables, which could limit their predictive potential. In the present study, the predictive performances in terms of CPV enteritis survival likelihood of an operator-validated logistic regression were compared with those of more flexible methods featured by automatic variable selection. Several anamnestic, clinical, haematological and biochemical parameters were collected from 134 dogs at admission in a veterinary practice. Animal status was monitored until dismissal or death (mortality=21.6%). The best automatic variable selection method (random forest) showed excellent discriminatory capabilities (AUC=0.997, sensitivity=0.941 and specificity=1) compared with the logistic regression model (AUC=0.831, sensitivity=0.882 and specificity=0.652), when evaluated on a fully independent test data set. The implemented approaches allowed to identify antithrombin, serum aspartate aminotransferase, serum lipase, monocyte and lymphocyte count as the clinical parameter combination with the highest predictive capability, thus limiting the panel of required tests. The model validated in the present study allows prompt prediction of disease severity at admission and provides objective and reliable criteria to support the clinician in selection of the therapeutic approach.
Sections du résumé
BACKGROUND
BACKGROUND
Canine parvovirus (CPV) represents one of the major infections in dogs. While supportive therapy significantly reduces mortality, other approaches have been reported to provide significant benefits. Unfortunately, the high cost of these treatments is typically a limiting factor. Consequently, a reliable prognostic tool allowing for an informed therapeutic approach would be of great interest. However, current methods are essentially based on 'a priori' selection of predictive variables, which could limit their predictive potential.
METHODS
METHODS
In the present study, the predictive performances in terms of CPV enteritis survival likelihood of an operator-validated logistic regression were compared with those of more flexible methods featured by automatic variable selection. Several anamnestic, clinical, haematological and biochemical parameters were collected from 134 dogs at admission in a veterinary practice. Animal status was monitored until dismissal or death (mortality=21.6%).
RESULTS
RESULTS
The best automatic variable selection method (random forest) showed excellent discriminatory capabilities (AUC=0.997, sensitivity=0.941 and specificity=1) compared with the logistic regression model (AUC=0.831, sensitivity=0.882 and specificity=0.652), when evaluated on a fully independent test data set. The implemented approaches allowed to identify antithrombin, serum aspartate aminotransferase, serum lipase, monocyte and lymphocyte count as the clinical parameter combination with the highest predictive capability, thus limiting the panel of required tests.
CONCLUSION
CONCLUSIONS
The model validated in the present study allows prompt prediction of disease severity at admission and provides objective and reliable criteria to support the clinician in selection of the therapeutic approach.
Identifiants
pubmed: 32169946
pii: vr.105283
doi: 10.1136/vr.105283
doi:
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e76Informations de copyright
© British Veterinary Association 2020. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.